arXiv: Signal Processing最新文献_第2页

A Review on Coexistence Issues of Broadband Millimeter-Wave Communications 宽带毫米波通信共存问题研究进展

arXiv: Signal Processing Pub Date : 2020-08-20 DOI: 10.36227/techrxiv.16434693

Ching-Lun Tai, D. Wu

{"title":"A Review on Coexistence Issues of Broadband Millimeter-Wave Communications","authors":"Ching-Lun Tai, D. Wu","doi":"10.36227/techrxiv.16434693","DOIUrl":"https://doi.org/10.36227/techrxiv.16434693","url":null,"abstract":"With higher frequencies and broader spectrum than conventional frequency bands, the millimeter-wave (mmWave) band is suitable for next-generation wireless networks featuring short-distance high-rate communications. As a newcomer, mmWaves are expected to have the backward compatibility with existing services and collaborate with other technologies in order to enhance system performances. Therefore, the coexistence issues become an essential topic for next-generation wireless communications. In this paper, we systematically review the coexistence issues of broadband mmWave communications and their corresponding solutions proposed in the literature, helping shed light on the insights of the mmWave design. Particularly, the works surveyed in this paper can be classiﬁed into four categories: coexistence with microwave communications, coexistence with ﬁxed services, coexistence with non-orthogonal multiple access (NOMA), and other coexistence issues. Results of numerical evaluations inspired by the literature are presented for a deeper analysis. We also point out some challenges and future directions for each category as a roadmap to further investigate the coexistence issues of broadband mmWave communications.","PeriodicalId":8487,"journal":{"name":"arXiv: Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79027761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resource Allocation in 6G Optical Wireless Systems 6G光无线系统中的资源分配

arXiv: Signal Processing Pub Date : 2020-08-05 DOI: 10.1007/978-3-030-72777-2_10

O. Z. Alsulami, T. El-Gorashi, J. Elmirghani

引用次数: 4

Personal Identification Using Ultrawideband Radar Measurement of Walking and Sitting Motions and a Convolutional Neural Network 使用超宽带雷达测量行走和坐姿运动和卷积神经网络的个人识别

arXiv: Signal Processing Pub Date : 2020-08-05 DOI: 10.1587/transinf.2018EDP7435

T. Sakamoto

引用次数: 7

Application of the Non-Hermitian Singular Spectrum Analysis to the Exponential Retrieval Problem 非厄米奇异谱分析在指数检索问题中的应用

arXiv: Signal Processing Pub Date : 2020-07-21 DOI: 10.32603/1993-8985-2020-23-3-6-24

D. Nicolsky, G. Tipenko

引用次数: 0

Real-time 112 Gbit/s DMT for Data Center Interconnects 数据中心互连实时112gbit /s DMT

arXiv: Signal Processing Pub Date : 2020-06-08 DOI: 10.1364/SPPCOM.2018.SpTh4F.3

A. Dochhan, Nicklas Eiselt, J. Zou, H. Griesser, M. Eiselt, J. Elbers

引用次数: 2

When Full-Duplex Transmission Meets Intelligent Reflecting Surface: Opportunities and Challenges 全双工传输与智能反射面:机遇与挑战

arXiv: Signal Processing Pub Date : 2020-05-26 DOI: 10.36227/techrxiv.12369152

Gaofeng Pan, Jia Ye, Jianping An, Mohamed-Slim Alouini

引用次数: 14

Apply VGGNet-Based Deep Learning Model of Vibration Data for Prediction Model of Gravity Acceleration Equipment 基于vggnet的振动数据深度学习模型在重力加速度设备预测模型中的应用

arXiv: Signal Processing Pub Date : 2020-05-22 DOI: 10.20944/preprints202012.0646.v1

Seonwoo Lee, HyeonTak Yu, HoJun Yang, Inseo Song, JaeHeung Yang, Gang-Min Lim, Kyusung Kim, Byeong-Keun Choi, Jangwoo Kwon

{"title":"Apply VGGNet-Based Deep Learning Model of Vibration Data for Prediction Model of Gravity Acceleration Equipment","authors":"Seonwoo Lee, HyeonTak Yu, HoJun Yang, Inseo Song, JaeHeung Yang, Gang-Min Lim, Kyusung Kim, Byeong-Keun Choi, Jangwoo Kwon","doi":"10.20944/preprints202012.0646.v1","DOIUrl":"https://doi.org/10.20944/preprints202012.0646.v1","url":null,"abstract":"Hypergravity accelerators are a type of large machinery used for gravity training or medical research. A failure of such large equipment can be a serious problem in terms of safety or costs. This paper proposes a prediction model that can proactively prevent failures that may occur in a hy-pergravity accelerator. The method proposed in this paper was to convert vibration signals to spectograms and perform classification training using a deep learning model. An experiment was conducted to evaluate the performance of the method proposed in this paper. A 4-channel accel-erometer was attached to the bearing housing, which is a rotor, and time-amplitude data were obtained from the measured values by sampling. The data were converted to a two-dimensional spectrogram, and classification training was performed using a deep learning model for four conditions of the equipment: Unbalance, Misalignment, Shaft Rubbing, and Normal. The ex-perimental results showed that the proposed method had a 99.5% F1-Score, which was up to 23% higher than the 76.25% for existing feature-based learning models.","PeriodicalId":8487,"journal":{"name":"arXiv: Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90120195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An experimental study on airborne landmine detection using a circular synthetic aperture radar 圆形合成孔径雷达机载地雷探测实验研究

arXiv: Signal Processing Pub Date : 2020-05-06 DOI: 10.18725/OPARU-31981

Markus Schartel, Ralf Burr, Rik Bahnemann, W. Mayer, C. Waldschmidt

{"title":"An experimental study on airborne landmine detection using a circular synthetic aperture radar","authors":"Markus Schartel, Ralf Burr, Rik Bahnemann, W. Mayer, C. Waldschmidt","doi":"10.18725/OPARU-31981","DOIUrl":"https://doi.org/10.18725/OPARU-31981","url":null,"abstract":"Many countries in the world are contaminated with landmines. Several thousand casualties occur every year. Although there are certain types of mines that can be detected from a safe stand-off position with tools, humanitarian demining is still mostly done by hand. As a new approach, an unmanned aerial system (UAS) equipped with a ground penetrating synthetic aperture radar (GPSAR) was developed, which is used to detect landmines, cluster munition, grenades, and improvised explosive devices (IEDs). The measurement system consists of a multicopter, a total station, an inertial measurement unit (IMU), and a frequency-modulated continuous-wave (FMCW) radar operating from 1 GHz to 4 GHz. The highly accurate localization of the measurement system and the full flexibility of the UAS are used to generate 3D-repeat-pass circular SAR images of buried antipersonnel landmines. In order to demonstrate the functionality of the system, 15 different dummy landmines were buried in a sandbox. The measurement results show the high potential of circular SAR for the detection of minimum metal mines. 11 out of 15 different test objects could be detected unambiguously with cm-level accuracy by examining depth profiles showing the amplitude of the targets response over the processing depth.","PeriodicalId":8487,"journal":{"name":"arXiv: Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90729746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 21

A Software-Based Approach for Acoustical Modeling of Construction Job Sites with Multiple Operational Machines 基于软件的多操作机器施工现场声学建模方法

arXiv: Signal Processing Pub Date : 2020-04-25 DOI: 10.1061/9780784482865.094

B. Sherafat, Abbas Rashidi, Siyuan Song

{"title":"A Software-Based Approach for Acoustical Modeling of Construction Job Sites with Multiple Operational Machines","authors":"B. Sherafat, Abbas Rashidi, Siyuan Song","doi":"10.1061/9780784482865.094","DOIUrl":"https://doi.org/10.1061/9780784482865.094","url":null,"abstract":"Several studies have been conducted to automatically recognize activities of construction equipment using their generated sound patterns. Most of these studies are focused on single-machine scenarios under controlled environments. However, real construction job sites are more complex and often consist of several types of equipment with different orientations, directions, and locations working simultaneously. The current state-of-research for recognizing activities of multiple machines on a job site is hardware-oriented, on the basis of using microphone arrays (i.e., several single microphones installed on a board under specific geometric layout) and beamforming principles for classifying sound directions for each machine. While effective, the common hardware-approach has limitations and using microphone arrays is not always a feasible option at ordinary job sites. In this paper, the authors proposed a software-oriented approach using Deep Neural Networks (DNNs) and Time-Frequency Masks (TFMs) to address this issue. The proposed method requires using single microphones, as the sound sources could be differentiated by training a DNN. The presented approach has been tested and validated under simulated job site conditions where two machines operated simultaneously. Results show that the average accuracy for soft TFM is 38% higher than binary TFM.","PeriodicalId":8487,"journal":{"name":"arXiv: Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80021349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Spectral mapping of natural signals 自然信号的频谱映射

arXiv: Signal Processing Pub Date : 2020-04-21 DOI: 10.31234/OSF.IO/8VQ3G

Rahman Ms

引用次数: 0